Overview

Dataset statistics

Number of variables15
Number of observations660
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory77.5 KiB
Average record size in memory120.2 B

Variable types

Categorical5
Numeric10

Alerts

Club has a high cardinality: 180 distinct valuesHigh cardinality
Player Names has a high cardinality: 444 distinct valuesHigh cardinality
Matches_Played is highly overall correlated with Mins and 4 other fieldsHigh correlation
Mins is highly overall correlated with Matches_Played and 4 other fieldsHigh correlation
Goals is highly overall correlated with Matches_Played and 4 other fieldsHigh correlation
xG is highly overall correlated with Matches_Played and 4 other fieldsHigh correlation
xG Per Avg Match is highly overall correlated with Shots Per Avg Match and 1 other fieldsHigh correlation
Shots is highly overall correlated with Matches_Played and 4 other fieldsHigh correlation
OnTarget is highly overall correlated with Matches_Played and 4 other fieldsHigh correlation
Shots Per Avg Match is highly overall correlated with xG Per Avg Match and 1 other fieldsHigh correlation
On Target Per Avg Match is highly overall correlated with xG Per Avg Match and 1 other fieldsHigh correlation
Country is highly overall correlated with LeagueHigh correlation
League is highly overall correlated with CountryHigh correlation
Player Names is uniformly distributedUniform
Substitution has 169 (25.6%) zerosZeros

Reproduction

Analysis started2023-08-03 01:35:18.412083
Analysis finished2023-08-03 01:35:56.720883
Duration38.31 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Country
Categorical

Distinct9
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
Spain
100 
Italy
100 
Germany
100 
Brazil
100 
England
80 
Other values (4)
180 

Length

Max length12
Median length9
Mean length6.3333333
Min length3

Characters and Unicode

Total characters4180
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpain
2nd rowSpain
3rd rowSpain
4th rowSpain
5th rowSpain

Common Values

ValueCountFrequency (%)
Spain 100
15.2%
Italy 100
15.2%
Germany 100
15.2%
Brazil 100
15.2%
England 80
12.1%
France 60
9.1%
USA 40
 
6.1%
Portugal 40
 
6.1%
Netherlands 40
 
6.1%

Length

2023-08-03T01:35:57.340084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-03T01:35:57.652509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
spain 100
15.2%
italy 100
15.2%
germany 100
15.2%
brazil 100
15.2%
england 80
12.1%
france 60
9.1%
usa 40
 
6.1%
portugal 40
 
6.1%
netherlands 40
 
6.1%

Most occurring characters

ValueCountFrequency (%)
a 620
14.8%
n 460
 
11.0%
l 360
 
8.6%
r 340
 
8.1%
e 240
 
5.7%
i 200
 
4.8%
y 200
 
4.8%
t 180
 
4.3%
S 140
 
3.3%
d 120
 
2.9%
Other values (19) 1320
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3360
80.4%
Uppercase Letter 740
 
17.7%
Space Separator 80
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 620
18.5%
n 460
13.7%
l 360
10.7%
r 340
10.1%
e 240
 
7.1%
i 200
 
6.0%
y 200
 
6.0%
t 180
 
5.4%
d 120
 
3.6%
g 120
 
3.6%
Other values (8) 520
15.5%
Uppercase Letter
ValueCountFrequency (%)
S 140
18.9%
B 100
13.5%
G 100
13.5%
I 100
13.5%
E 80
10.8%
F 60
8.1%
U 40
 
5.4%
A 40
 
5.4%
P 40
 
5.4%
N 40
 
5.4%
Space Separator
ValueCountFrequency (%)
80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4100
98.1%
Common 80
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 620
15.1%
n 460
 
11.2%
l 360
 
8.8%
r 340
 
8.3%
e 240
 
5.9%
i 200
 
4.9%
y 200
 
4.9%
t 180
 
4.4%
S 140
 
3.4%
d 120
 
2.9%
Other values (18) 1240
30.2%
Common
ValueCountFrequency (%)
80
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 620
14.8%
n 460
 
11.0%
l 360
 
8.6%
r 340
 
8.1%
e 240
 
5.7%
i 200
 
4.8%
y 200
 
4.8%
t 180
 
4.3%
S 140
 
3.3%
d 120
 
2.9%
Other values (19) 1320
31.6%

League
Categorical

Distinct28
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
La Liga
100 
Serie A
100 
Bundesliga
100 
Campeonato Brasileiro Série A
100 
Premier League
80 
Other values (23)
180 

Length

Max length30
Median length15
Mean length12.777273
Min length3

Characters and Unicode

Total characters8433
Distinct characters38
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLa Liga
2nd rowLa Liga
3rd rowLa Liga
4th rowLa Liga
5th rowLa Liga

Common Values

ValueCountFrequency (%)
La Liga 100
15.2%
Serie A 100
15.2%
Bundesliga 100
15.2%
Campeonato Brasileiro Série A 100
15.2%
Premier League 80
12.1%
Primeira Liga 40
 
6.1%
MLS 40
 
6.1%
Eredivisie 40
 
6.1%
France Ligue 12 3
 
0.5%
France Ligue 9 3
 
0.5%
Other values (18) 54
8.2%

Length

2023-08-03T01:35:57.975104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 200
14.3%
liga 140
10.0%
la 100
 
7.1%
serie 100
 
7.1%
bundesliga 100
 
7.1%
campeonato 100
 
7.1%
brasileiro 100
 
7.1%
sã©rie 100
 
7.1%
premier 80
 
5.7%
league 80
 
5.7%
Other values (25) 300
21.4%

Most occurring characters

ValueCountFrequency (%)
e 1160
13.8%
i 980
11.6%
a 820
 
9.7%
740
 
8.8%
r 740
 
8.8%
L 420
 
5.0%
g 380
 
4.5%
o 300
 
3.6%
n 260
 
3.1%
S 240
 
2.8%
Other values (28) 2393
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5980
70.9%
Uppercase Letter 1520
 
18.0%
Space Separator 740
 
8.8%
Other Symbol 100
 
1.2%
Decimal Number 93
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1160
19.4%
i 980
16.4%
a 820
13.7%
r 740
12.4%
g 380
 
6.4%
o 300
 
5.0%
n 260
 
4.3%
s 240
 
4.0%
u 240
 
4.0%
m 220
 
3.7%
Other values (6) 640
10.7%
Uppercase Letter
ValueCountFrequency (%)
L 420
27.6%
S 240
15.8%
A 200
13.2%
B 200
13.2%
P 120
 
7.9%
à 100
 
6.6%
C 100
 
6.6%
F 60
 
3.9%
M 40
 
2.6%
E 40
 
2.6%
Decimal Number
ValueCountFrequency (%)
1 36
38.7%
2 9
 
9.7%
9 6
 
6.5%
0 6
 
6.5%
7 6
 
6.5%
8 6
 
6.5%
5 6
 
6.5%
3 6
 
6.5%
4 6
 
6.5%
6 6
 
6.5%
Space Separator
ValueCountFrequency (%)
740
100.0%
Other Symbol
ValueCountFrequency (%)
© 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7500
88.9%
Common 933
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1160
15.5%
i 980
13.1%
a 820
10.9%
r 740
 
9.9%
L 420
 
5.6%
g 380
 
5.1%
o 300
 
4.0%
n 260
 
3.5%
S 240
 
3.2%
s 240
 
3.2%
Other values (16) 1960
26.1%
Common
ValueCountFrequency (%)
740
79.3%
© 100
 
10.7%
1 36
 
3.9%
2 9
 
1.0%
9 6
 
0.6%
0 6
 
0.6%
7 6
 
0.6%
8 6
 
0.6%
5 6
 
0.6%
3 6
 
0.6%
Other values (2) 12
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8233
97.6%
None 200
 
2.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1160
14.1%
i 980
11.9%
a 820
 
10.0%
740
 
9.0%
r 740
 
9.0%
L 420
 
5.1%
g 380
 
4.6%
o 300
 
3.6%
n 260
 
3.2%
S 240
 
2.9%
Other values (26) 2193
26.6%
None
ValueCountFrequency (%)
à 100
50.0%
© 100
50.0%

Club
Categorical

Distinct180
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
None
 
34
(PSG)
 
14
(BAR)
 
13
(NAP)
 
13
(RMA)
 
11
Other values (175)
575 

Length

Max length29
Median length5
Mean length4.9984848
Min length3

Characters and Unicode

Total characters3299
Distinct characters46
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)9.7%

Sample

1st row(BET)
2nd row(BAR)
3rd row(ATL)
4th row(CAR)
5th row(VAL)

Common Values

ValueCountFrequency (%)
None 34
 
5.2%
(PSG) 14
 
2.1%
(BAR) 13
 
2.0%
(NAP) 13
 
2.0%
(RMA) 11
 
1.7%
(SOC) 11
 
1.7%
(ATA) 11
 
1.7%
(FLA) 11
 
1.7%
(TOT) 11
 
1.7%
(INT) 10
 
1.5%
Other values (170) 521
78.9%

Length

2023-08-03T01:35:58.254591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none 34
 
5.1%
psg 14
 
2.1%
bar 13
 
2.0%
nap 13
 
2.0%
rma 11
 
1.7%
soc 11
 
1.7%
ata 11
 
1.7%
fla 11
 
1.7%
tot 11
 
1.7%
liv 10
 
1.5%
Other values (174) 526
79.1%

Most occurring characters

ValueCountFrequency (%)
) 616
18.7%
( 616
18.7%
A 238
 
7.2%
R 138
 
4.2%
L 130
 
3.9%
N 126
 
3.8%
S 122
 
3.7%
O 120
 
3.6%
E 118
 
3.6%
C 107
 
3.2%
Other values (36) 968
29.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1888
57.2%
Close Punctuation 616
 
18.7%
Open Punctuation 616
 
18.7%
Lowercase Letter 173
 
5.2%
Space Separator 5
 
0.2%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 238
 
12.6%
R 138
 
7.3%
L 130
 
6.9%
N 126
 
6.7%
S 122
 
6.5%
O 120
 
6.4%
E 118
 
6.2%
C 107
 
5.7%
M 92
 
4.9%
T 91
 
4.8%
Other values (14) 606
32.1%
Lowercase Letter
ValueCountFrequency (%)
o 44
25.4%
n 43
24.9%
e 34
19.7%
i 9
 
5.2%
r 8
 
4.6%
a 8
 
4.6%
l 4
 
2.3%
u 3
 
1.7%
c 3
 
1.7%
g 3
 
1.7%
Other values (8) 14
 
8.1%
Close Punctuation
ValueCountFrequency (%)
) 616
100.0%
Open Punctuation
ValueCountFrequency (%)
( 616
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2061
62.5%
Common 1238
37.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 238
 
11.5%
R 138
 
6.7%
L 130
 
6.3%
N 126
 
6.1%
S 122
 
5.9%
O 120
 
5.8%
E 118
 
5.7%
C 107
 
5.2%
M 92
 
4.5%
T 91
 
4.4%
Other values (32) 779
37.8%
Common
ValueCountFrequency (%)
) 616
49.8%
( 616
49.8%
5
 
0.4%
. 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3299
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 616
18.7%
( 616
18.7%
A 238
 
7.2%
R 138
 
4.2%
L 130
 
3.9%
N 126
 
3.8%
S 122
 
3.7%
O 120
 
3.6%
E 118
 
3.6%
C 107
 
3.2%
Other values (36) 968
29.3%

Player Names
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct444
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
Andrea Belotti
 
5
Lionel Messi
 
5
Luis Suarez
 
5
Andrej Kramaric
 
5
Ciro Immobile
 
5
Other values (439)
635 

Length

Max length25
Median length21
Mean length12.762121
Min length2

Characters and Unicode

Total characters8423
Distinct characters58
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique317 ?
Unique (%)48.0%

Sample

1st rowJuanmi Callejon
2nd rowAntoine Griezmann
3rd rowLuis Suarez
4th rowRuben Castro
5th rowKevin Gameiro

Common Values

ValueCountFrequency (%)
Andrea Belotti 5
 
0.8%
Lionel Messi 5
 
0.8%
Luis Suarez 5
 
0.8%
Andrej Kramaric 5
 
0.8%
Ciro Immobile 5
 
0.8%
Cristiano Ronaldo 5
 
0.8%
Robert Lewandowski 5
 
0.8%
Timo Werner 5
 
0.8%
Iago Aspas 5
 
0.8%
Fabio Quagliarella 5
 
0.8%
Other values (434) 610
92.4%

Length

2023-08-03T01:35:58.546364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
diego 9
 
0.7%
bruno 9
 
0.7%
kevin 8
 
0.6%
luis 8
 
0.6%
carlos 8
 
0.6%
raul 7
 
0.6%
fabio 7
 
0.6%
iago 7
 
0.6%
andrea 7
 
0.6%
de 7
 
0.6%
Other values (698) 1173
93.8%

Most occurring characters

ValueCountFrequency (%)
a 834
 
9.9%
e 765
 
9.1%
679
 
8.1%
i 625
 
7.4%
o 587
 
7.0%
r 577
 
6.9%
n 545
 
6.5%
l 373
 
4.4%
s 308
 
3.7%
u 262
 
3.1%
Other values (48) 2868
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6443
76.5%
Uppercase Letter 1272
 
15.1%
Space Separator 679
 
8.1%
Dash Punctuation 22
 
0.3%
Other Punctuation 4
 
< 0.1%
Control 2
 
< 0.1%
Format 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 834
12.9%
e 765
11.9%
i 625
9.7%
o 587
9.1%
r 577
9.0%
n 545
8.5%
l 373
 
5.8%
s 308
 
4.8%
u 262
 
4.1%
d 227
 
3.5%
Other values (16) 1340
20.8%
Uppercase Letter
ValueCountFrequency (%)
M 127
 
10.0%
A 102
 
8.0%
S 86
 
6.8%
D 79
 
6.2%
B 75
 
5.9%
L 74
 
5.8%
R 74
 
5.8%
G 71
 
5.6%
C 69
 
5.4%
P 69
 
5.4%
Other values (16) 446
35.1%
Other Punctuation
ValueCountFrequency (%)
' 2
50.0%
. 2
50.0%
Space Separator
ValueCountFrequency (%)
679
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Control
ValueCountFrequency (%)
 2
100.0%
Format
ValueCountFrequency (%)
­ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7715
91.6%
Common 708
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 834
 
10.8%
e 765
 
9.9%
i 625
 
8.1%
o 587
 
7.6%
r 577
 
7.5%
n 545
 
7.1%
l 373
 
4.8%
s 308
 
4.0%
u 262
 
3.4%
d 227
 
2.9%
Other values (42) 2612
33.9%
Common
ValueCountFrequency (%)
679
95.9%
- 22
 
3.1%
' 2
 
0.3%
 2
 
0.3%
. 2
 
0.3%
­ 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8418
99.9%
None 5
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 834
 
9.9%
e 765
 
9.1%
679
 
8.1%
i 625
 
7.4%
o 587
 
7.0%
r 577
 
6.9%
n 545
 
6.5%
l 373
 
4.4%
s 308
 
3.7%
u 262
 
3.1%
Other values (45) 2863
34.0%
None
ValueCountFrequency (%)
à 2
40.0%
 2
40.0%
­ 1
20.0%

Matches_Played
Real number (ℝ)

Distinct37
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.371212
Minimum2
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-08-03T01:35:58.817003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q114
median24
Q331
95-th percentile36
Maximum38
Range36
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.7546575
Coefficient of variation (CV)0.43603616
Kurtosis-1.1303255
Mean22.371212
Median Absolute Deviation (MAD)7
Skewness-0.36923941
Sum14765
Variance95.153343
MonotonicityNot monotonic
2023-08-03T01:35:59.100306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
31 32
 
4.8%
9 32
 
4.8%
32 30
 
4.5%
29 30
 
4.5%
23 30
 
4.5%
33 29
 
4.4%
26 28
 
4.2%
10 26
 
3.9%
24 26
 
3.9%
27 25
 
3.8%
Other values (27) 372
56.4%
ValueCountFrequency (%)
2 1
 
0.2%
3 5
 
0.8%
4 3
 
0.5%
5 6
 
0.9%
6 24
3.6%
7 22
3.3%
8 24
3.6%
9 32
4.8%
10 26
3.9%
11 12
 
1.8%
ValueCountFrequency (%)
38 4
 
0.6%
37 13
2.0%
36 17
2.6%
35 24
3.6%
34 21
3.2%
33 29
4.4%
32 30
4.5%
31 32
4.8%
30 24
3.6%
29 30
4.5%

Substitution
Real number (ℝ)

Distinct21
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2242424
Minimum0
Maximum26
Zeros169
Zeros (%)25.6%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-08-03T01:35:59.369799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile11
Maximum26
Range26
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.8394984
Coefficient of variation (CV)1.1908219
Kurtosis4.8963123
Mean3.2242424
Median Absolute Deviation (MAD)2
Skewness1.9221552
Sum2128
Variance14.741748
MonotonicityNot monotonic
2023-08-03T01:35:59.632860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 169
25.6%
1 130
19.7%
2 80
12.1%
3 65
 
9.8%
5 43
 
6.5%
4 35
 
5.3%
6 34
 
5.2%
7 21
 
3.2%
8 20
 
3.0%
10 13
 
2.0%
Other values (11) 50
 
7.6%
ValueCountFrequency (%)
0 169
25.6%
1 130
19.7%
2 80
12.1%
3 65
 
9.8%
4 35
 
5.3%
5 43
 
6.5%
6 34
 
5.2%
7 21
 
3.2%
8 20
 
3.0%
9 10
 
1.5%
ValueCountFrequency (%)
26 1
 
0.2%
23 2
 
0.3%
18 3
 
0.5%
17 1
 
0.2%
16 1
 
0.2%
15 5
0.8%
14 2
 
0.3%
13 5
0.8%
12 10
1.5%
11 10
1.5%

Mins
Real number (ℝ)

Distinct583
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2071.4167
Minimum264
Maximum4177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-08-03T01:35:59.921657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum264
5-th percentile574.95
Q11363.5
median2245.5
Q32822
95-th percentile3271.25
Maximum4177
Range3913
Interquartile range (IQR)1458.5

Descriptive statistics

Standard deviation900.59505
Coefficient of variation (CV)0.43477252
Kurtosis-1.0524114
Mean2071.4167
Median Absolute Deviation (MAD)659
Skewness-0.35736066
Sum1367135
Variance811071.44
MonotonicityNot monotonic
2023-08-03T01:36:00.241746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3115 3
 
0.5%
2312 3
 
0.5%
872 3
 
0.5%
669 3
 
0.5%
567 3
 
0.5%
822 3
 
0.5%
903 3
 
0.5%
1786 2
 
0.3%
2822 2
 
0.3%
3236 2
 
0.3%
Other values (573) 633
95.9%
ValueCountFrequency (%)
264 1
0.2%
280 1
0.2%
293 1
0.2%
337 1
0.2%
356 1
0.2%
387 1
0.2%
396 1
0.2%
397 1
0.2%
451 1
0.2%
452 1
0.2%
ValueCountFrequency (%)
4177 1
0.2%
3931 1
0.2%
3651 1
0.2%
3641 1
0.2%
3555 1
0.2%
3533 2
0.3%
3511 1
0.2%
3491 1
0.2%
3474 1
0.2%
3448 1
0.2%

Goals
Real number (ℝ)

Distinct34
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.810606
Minimum2
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-08-03T01:36:00.545392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q18
median11
Q314
95-th percentile23
Maximum42
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.0753146
Coefficient of variation (CV)0.51439482
Kurtosis2.8916838
Mean11.810606
Median Absolute Deviation (MAD)3
Skewness1.2868065
Sum7795
Variance36.909447
MonotonicityNot monotonic
2023-08-03T01:36:00.828681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
11 67
 
10.2%
12 64
 
9.7%
10 55
 
8.3%
9 50
 
7.6%
4 48
 
7.3%
13 47
 
7.1%
14 37
 
5.6%
8 36
 
5.5%
15 30
 
4.5%
16 29
 
4.4%
Other values (24) 197
29.8%
ValueCountFrequency (%)
2 9
 
1.4%
3 14
 
2.1%
4 48
7.3%
5 28
4.2%
6 20
 
3.0%
7 25
 
3.8%
8 36
5.5%
9 50
7.6%
10 55
8.3%
11 67
10.2%
ValueCountFrequency (%)
42 1
 
0.2%
37 1
 
0.2%
36 3
0.5%
34 1
 
0.2%
33 2
 
0.3%
31 3
0.5%
30 1
 
0.2%
29 5
0.8%
28 4
0.6%
26 3
0.5%

xG
Real number (ℝ)

Distinct558
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.089606
Minimum0.71
Maximum32.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-08-03T01:36:01.143750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.71
5-th percentile2.2775
Q16.1
median9.285
Q313.2525
95-th percentile20.5105
Maximum32.54
Range31.83
Interquartile range (IQR)7.1525

Descriptive statistics

Standard deviation5.7248437
Coefficient of variation (CV)0.56740012
Kurtosis1.2658161
Mean10.089606
Median Absolute Deviation (MAD)3.45
Skewness0.95635774
Sum6659.14
Variance32.773835
MonotonicityNot monotonic
2023-08-03T01:36:01.434187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.62 4
 
0.6%
3.33 4
 
0.6%
6.19 3
 
0.5%
6.11 3
 
0.5%
11.12 3
 
0.5%
14.51 3
 
0.5%
8.56 3
 
0.5%
8.7 3
 
0.5%
9.39 3
 
0.5%
11.09 3
 
0.5%
Other values (548) 628
95.2%
ValueCountFrequency (%)
0.71 1
0.2%
0.8 1
0.2%
0.96 1
0.2%
1.03 1
0.2%
1.05 1
0.2%
1.12 1
0.2%
1.13 1
0.2%
1.31 1
0.2%
1.39 1
0.2%
1.43 1
0.2%
ValueCountFrequency (%)
32.54 1
0.2%
31.17 1
0.2%
31.05 1
0.2%
30.6 1
0.2%
30.52 1
0.2%
29.27 1
0.2%
29 1
0.2%
28.94 1
0.2%
27.32 1
0.2%
26.65 1
0.2%

xG Per Avg Match
Real number (ℝ)

Distinct92
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47616667
Minimum0.07
Maximum1.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-08-03T01:36:01.730325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.24
Q10.34
median0.435
Q30.57
95-th percentile0.8605
Maximum1.35
Range1.28
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation0.19283132
Coefficient of variation (CV)0.40496602
Kurtosis1.9127398
Mean0.47616667
Median Absolute Deviation (MAD)0.105
Skewness1.1766357
Sum314.27
Variance0.037183918
MonotonicityNot monotonic
2023-08-03T01:36:02.070592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.33 23
 
3.5%
0.39 21
 
3.2%
0.37 21
 
3.2%
0.4 20
 
3.0%
0.41 20
 
3.0%
0.43 19
 
2.9%
0.34 18
 
2.7%
0.57 18
 
2.7%
0.31 16
 
2.4%
0.45 16
 
2.4%
Other values (82) 468
70.9%
ValueCountFrequency (%)
0.07 1
 
0.2%
0.09 1
 
0.2%
0.15 2
 
0.3%
0.16 5
0.8%
0.17 1
 
0.2%
0.18 4
0.6%
0.19 2
 
0.3%
0.2 1
 
0.2%
0.21 4
0.6%
0.22 4
0.6%
ValueCountFrequency (%)
1.35 1
 
0.2%
1.27 1
 
0.2%
1.19 2
0.3%
1.16 1
 
0.2%
1.12 1
 
0.2%
1.1 1
 
0.2%
1.08 1
 
0.2%
1.06 4
0.6%
1.05 1
 
0.2%
1.01 1
 
0.2%

Shots
Real number (ℝ)

Distinct144
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.177273
Minimum5
Maximum208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-08-03T01:36:02.390437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile14
Q137.75
median62
Q386
95-th percentile124.05
Maximum208
Range203
Interquartile range (IQR)48.25

Descriptive statistics

Standard deviation34.941622
Coefficient of variation (CV)0.54445476
Kurtosis0.62646478
Mean64.177273
Median Absolute Deviation (MAD)24
Skewness0.67615024
Sum42357
Variance1220.9169
MonotonicityNot monotonic
2023-08-03T01:36:02.702015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 13
 
2.0%
21 13
 
2.0%
18 12
 
1.8%
55 12
 
1.8%
52 11
 
1.7%
81 11
 
1.7%
87 10
 
1.5%
56 10
 
1.5%
80 9
 
1.4%
54 9
 
1.4%
Other values (134) 550
83.3%
ValueCountFrequency (%)
5 2
 
0.3%
6 2
 
0.3%
7 2
 
0.3%
8 1
 
0.2%
9 2
 
0.3%
10 3
0.5%
11 3
0.5%
12 5
0.8%
13 7
1.1%
14 7
1.1%
ValueCountFrequency (%)
208 1
0.2%
197 1
0.2%
179 1
0.2%
178 2
0.3%
177 1
0.2%
170 1
0.2%
167 1
0.2%
162 1
0.2%
159 2
0.3%
152 1
0.2%

OnTarget
Real number (ℝ)

Distinct79
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.365152
Minimum2
Maximum102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-08-03T01:36:03.029404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q117
median26
Q337
95-th percentile58
Maximum102
Range100
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.363149
Coefficient of variation (CV)0.57687509
Kurtosis2.365776
Mean28.365152
Median Absolute Deviation (MAD)10
Skewness1.1562839
Sum18721
Variance267.75265
MonotonicityNot monotonic
2023-08-03T01:36:03.342473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 26
 
3.9%
24 20
 
3.0%
23 20
 
3.0%
33 20
 
3.0%
19 20
 
3.0%
30 19
 
2.9%
8 19
 
2.9%
28 18
 
2.7%
17 17
 
2.6%
37 17
 
2.6%
Other values (69) 464
70.3%
ValueCountFrequency (%)
2 3
 
0.5%
3 4
 
0.6%
4 3
 
0.5%
5 12
1.8%
6 6
 
0.9%
7 17
2.6%
8 19
2.9%
9 14
2.1%
10 10
1.5%
11 15
2.3%
ValueCountFrequency (%)
102 1
0.2%
99 1
0.2%
98 1
0.2%
95 1
0.2%
91 1
0.2%
87 1
0.2%
86 1
0.2%
81 1
0.2%
79 1
0.2%
78 2
0.3%

Shots Per Avg Match
Real number (ℝ)

Distinct280
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9480152
Minimum0.8
Maximum7.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-08-03T01:36:03.787277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile1.709
Q12.335
median2.845
Q33.3825
95-th percentile4.54
Maximum7.2
Range6.4
Interquartile range (IQR)1.0475

Descriptive statistics

Standard deviation0.91490648
Coefficient of variation (CV)0.3103466
Kurtosis1.9853344
Mean2.9480152
Median Absolute Deviation (MAD)0.53
Skewness0.94730205
Sum1945.69
Variance0.83705387
MonotonicityNot monotonic
2023-08-03T01:36:04.181254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.47 8
 
1.2%
2.86 8
 
1.2%
2.75 7
 
1.1%
2.6 7
 
1.1%
3.05 7
 
1.1%
2.22 7
 
1.1%
2.65 6
 
0.9%
3.02 6
 
0.9%
2.49 6
 
0.9%
2.26 6
 
0.9%
Other values (270) 592
89.7%
ValueCountFrequency (%)
0.8 1
0.2%
0.81 1
0.2%
0.82 1
0.2%
0.85 1
0.2%
0.99 1
0.2%
1.03 1
0.2%
1.16 1
0.2%
1.2 2
0.3%
1.24 1
0.2%
1.27 1
0.2%
ValueCountFrequency (%)
7.2 1
0.2%
7.12 1
0.2%
6.32 1
0.2%
6.22 1
0.2%
5.99 1
0.2%
5.97 1
0.2%
5.89 1
0.2%
5.84 2
0.3%
5.67 1
0.2%
5.59 1
0.2%

On Target Per Avg Match
Real number (ℝ)

Distinct184
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3156515
Minimum0.24
Maximum3.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-08-03T01:36:04.690142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.24
5-th percentile0.72
Q10.98
median1.25
Q31.54
95-th percentile2.241
Maximum3.63
Range3.39
Interquartile range (IQR)0.56

Descriptive statistics

Standard deviation0.4742393
Coefficient of variation (CV)0.36045966
Kurtosis2.2392119
Mean1.3156515
Median Absolute Deviation (MAD)0.28
Skewness1.1809319
Sum868.33
Variance0.22490291
MonotonicityNot monotonic
2023-08-03T01:36:05.223671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 13
 
2.0%
0.94 12
 
1.8%
1.28 12
 
1.8%
1.21 12
 
1.8%
0.96 11
 
1.7%
1.3 10
 
1.5%
1.29 10
 
1.5%
1 10
 
1.5%
1.11 9
 
1.4%
1.26 9
 
1.4%
Other values (174) 552
83.6%
ValueCountFrequency (%)
0.24 1
 
0.2%
0.29 1
 
0.2%
0.4 1
 
0.2%
0.48 2
0.3%
0.5 1
 
0.2%
0.52 1
 
0.2%
0.54 1
 
0.2%
0.55 2
0.3%
0.56 4
0.6%
0.58 1
 
0.2%
ValueCountFrequency (%)
3.63 1
0.2%
3.11 2
0.3%
3.04 1
0.2%
2.94 1
0.2%
2.9 1
0.2%
2.89 2
0.3%
2.85 1
0.2%
2.84 1
0.2%
2.83 1
0.2%
2.77 1
0.2%

Year
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
2019
200 
2020
160 
2018
120 
2016
100 
2017
80 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2640
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2019 200
30.3%
2020 160
24.2%
2018 120
18.2%
2016 100
15.2%
2017 80
 
12.1%

Length

2023-08-03T01:36:05.736070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-03T01:36:06.102609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2019 200
30.3%
2020 160
24.2%
2018 120
18.2%
2016 100
15.2%
2017 80
 
12.1%

Most occurring characters

ValueCountFrequency (%)
2 820
31.1%
0 820
31.1%
1 500
18.9%
9 200
 
7.6%
8 120
 
4.5%
6 100
 
3.8%
7 80
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2640
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 820
31.1%
0 820
31.1%
1 500
18.9%
9 200
 
7.6%
8 120
 
4.5%
6 100
 
3.8%
7 80
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2640
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 820
31.1%
0 820
31.1%
1 500
18.9%
9 200
 
7.6%
8 120
 
4.5%
6 100
 
3.8%
7 80
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 820
31.1%
0 820
31.1%
1 500
18.9%
9 200
 
7.6%
8 120
 
4.5%
6 100
 
3.8%
7 80
 
3.0%

Interactions

2023-08-03T01:35:53.018273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:20.304378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:26.887279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:30.848501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:33.572301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:36.432724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:40.260475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:43.870134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:46.627943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:49.205640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:53.430869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:20.790861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:27.464236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:31.135229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:33.845565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:36.857061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:40.524230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:44.133656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:46.890400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:49.494114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:53.674354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:21.259609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:27.996519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:31.391561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:34.123797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:37.232461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:40.773023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:44.389021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:47.143069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:49.763056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:53.945204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:21.741134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:28.580081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:31.666841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:34.394957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:37.658820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:41.039858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:44.689201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:47.407321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:50.098883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:54.230060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:22.272049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:29.104042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:31.957155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:34.663139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:38.085256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:41.309882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:44.958424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:47.676945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:50.544094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:54.484517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:23.121517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:29.507151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:32.235089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:34.913063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:38.506838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:41.587252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:45.237662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:47.928680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:50.958369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:54.742516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:24.224862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:29.765239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:32.496153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:35.210131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:38.926833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:41.863191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:45.516620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:48.190193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:51.374691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:55.025836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:25.129633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:30.049882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:32.778105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:35.489522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:39.379832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:42.140745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:45.794056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:48.449740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:51.797537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:55.280353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:25.737675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:30.283698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:33.049551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:35.746891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:39.741366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:42.402647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:46.045304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:48.694082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:52.186798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:55.543377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:26.364116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:30.571083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:33.317761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:36.018154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:39.993765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:43.597751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:46.325359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:48.952804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T01:35:52.572865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-03T01:36:06.475203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Matches_PlayedSubstitutionMinsGoalsxGxG Per Avg MatchShotsOnTargetShots Per Avg MatchOn Target Per Avg MatchCountryLeagueYear
Matches_Played1.000-0.0330.9780.7340.752-0.1140.8250.791-0.009-0.0800.1910.1740.326
Substitution-0.0331.0000.0310.1580.1110.0030.1030.1040.007-0.0160.0000.0000.133
Mins0.9780.0311.0000.7420.766-0.1120.8350.796-0.014-0.0930.2070.1830.334
Goals0.7340.1580.7421.0000.8880.3200.8260.8640.2990.3060.1620.0910.308
xG0.7520.1110.7660.8881.0000.4630.8470.8620.3180.2820.1570.1210.288
xG Per Avg Match-0.1140.003-0.1120.3200.4631.0000.1590.2190.5670.6190.0410.1280.079
Shots0.8250.1030.8350.8260.8470.1591.0000.9430.4630.3020.1610.0870.323
OnTarget0.7910.1040.7960.8640.8620.2190.9431.0000.4190.4390.1460.0690.330
Shots Per Avg Match-0.0090.007-0.0140.2990.3180.5670.4630.4191.0000.8030.0770.1350.000
On Target Per Avg Match-0.080-0.016-0.0930.3060.2820.6190.3020.4390.8031.0000.1000.1370.093
Country0.1910.0000.2070.1620.1570.0410.1610.1460.0770.1001.0000.9850.287
League0.1740.0000.1830.0910.1210.1280.0870.0690.1350.1370.9851.0000.231
Year0.3260.1330.3340.3080.2880.0790.3230.3300.0000.0930.2870.2311.000

Missing values

2023-08-03T01:35:55.952020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-03T01:35:56.496845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CountryLeagueClubPlayer NamesMatches_PlayedSubstitutionMinsGoalsxGxG Per Avg MatchShotsOnTargetShots Per Avg MatchOn Target Per Avg MatchYear
0SpainLa Liga(BET)Juanmi Callejon19161849116.620.3448202.471.032016
1SpainLa Liga(BAR)Antoine Griezmann36031291611.860.3688412.671.242016
2SpainLa Liga(ATL)Luis Suarez34129402823.210.75120573.881.842016
3SpainLa Liga(CAR)Ruben Castro32328421314.060.47117423.911.402016
4SpainLa Liga(VAL)Kevin Gameiro211017451310.650.5850232.721.252016
5SpainLa Liga(JUV)Cristiano Ronaldo29026344224.680.89162605.842.162016
6SpainLa Liga(RMA)Karim Benzema23619671113.250.6469343.331.642016
7SpainLa Liga(PSG)Neymar30026941313.330.47105423.701.482016
8SpainLa Liga(CEL)Iago Aspas25723541913.880.5678373.151.492016
9SpainLa Liga(EIB)Sergi Enrich3172904118.250.2764262.090.852016
CountryLeagueClubPlayer NamesMatches_PlayedSubstitutionMinsGoalsxGxG Per Avg MatchShotsOnTargetShots Per Avg MatchOn Target Per Avg MatchYear
650NetherlandsEredivisie(AJA)Klaas-Jan Huntelaar61293896.910.7032173.241.722020
651NetherlandsEredivisie(WIL)Vangelis Pavlidis25022651112.640.5370312.941.302020
652NetherlandsEredivisie(EMM)Michael de Leeuw260238398.280.3351232.030.922020
653NetherlandsEredivisie(PSV)Donyell Malen1401245118.910.6859324.502.442020
654NetherlandsEredivisie(RZA)Haris Vuckic2322194116.000.2638171.650.742020
655NetherlandsEredivisie(UTR)Gyrano Kerk2402155107.490.3350182.200.792020
656NetherlandsEredivisie(AJA)Quincy Promes1821573129.770.5956303.381.812020
657NetherlandsEredivisie(PSV)Denzel Dumfries250236375.720.2345141.810.562020
658NetherlandsEredivisieNoneCyriel Dessers26024611514.510.5684433.241.662020
659NetherlandsEredivisie(PSV)Cody Gakpo1411155774.430.2738152.320.922020